Data Scientist

|| Urgent Hiring || Sr.Data Analyst || Hyderabad Location ||

Location- Hyderabad

Profile- Senior Data Scientist – Time Series & Data Analysis

Experience- 4+ Years

Ctc- upto 18 - 20 LPA lpa (Depends on interview)

Working days- 5 days (9:30am- 5:30 pm)

About the Role

We're looking for a Senior Data Scientist who can turn high-volume industrial sensor time series and event logs into forecasting, anomaly detection, and actionable intelligence for mission-critical infrastructure. Fundamentals-first — you should know the math behind your models, not just the tool. You'll have access to a great pool of AI native talent within an AI native company, working on cutting edge tools and projects with strong exposure to the latest in AI driven analytics.

Key Responsibilities

• Perform deep exploratory analysis on large, noisy, multi-channel sensor datasets
• Build robust preprocessing pipelines: cleaning, resampling, imputation, outlier handling, feature engineering
• Build statistical and ML models for forecasting, anomaly detection, change-point detection, and event classification
• Apply signal processing (FFT, wavelets, filtering) where appropriate
• Combine sensor time series with event/log data; define KPIs, thresholds, and alerting logic with product and engineering
• Translate analysis into insights, dashboards, and reports for technical and non-technical stakeholders
• Partner with data, ML, and software engineers to ship, monitor, and retrain models
• Mentor junior data scientists and analysts
• • Must Have Skills
• • 4+ yrs as a Data Scientist with models shipped to production
• Python (pandas, NumPy, SciPy, statsmodels) and strong SQL (PostgreSQL preferred)
• Math & statistics grounding: probability, statistics, linear algebra, optimization — able to explain loss functions, regularization,
• and evaluation metrics behind your models
• Time series: decomposition, ARIMA/state-space models, ML-based forecasting, anomaly & change-point detection on
• multivariate data
• Machine learning: scikit-learn, XGBoost/LightGBM, deep learning (PyTorch/TensorFlow); cross-validation, class imbalance,
• model interpretation
• Signal processing basics: FFT, wavelets, filtering
• Data visualization: Matplotlib, Plotly, Power BI/Tableau
• • EDA & preprocessing on large, noisy, multi-channel sensor data (cleaning, resampling, imputation, outlier handling)
• Git, clear communication to technical and non-technical stakeholders
• Willingness to learn and adopt AI tools within an advanced, AI native work ecosystem, including comfort using AI assisted
• analysis, copilots, and emerging AI driven workflows as part of daily work

Good-to-Have

• Advanced/applied statistics: experimental design, hypothesis testing, Bayesian statistics, causal inference
• NLP on logs/free-text (classification, entity extraction, summarization)
• Sensor/IoT or industrial monitoring domain experience
• Cloud ML (Azure ML/AWS SageMaker) + MLOps (MLflow, Docker, model monitoring)
• Advanced signal processing, streaming analytics
• Airflow, Spark, time series databases (TimescaleDB, InfluxDB)
• • What We Expect
• Own a problem from framing through deployed, monitored model
• Reach for the simplest method that works and defend why a complex one is needed
• Validate your own results before presenting; quantify uncertainty
• Justify method choice based on data characteristics and math, not tool familiarity
• Proactively surface data quality issues, leakage risks, and modeling blind spots
• Mentor others on analytical and ML best practicesEducation

Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative discipline. Equivalent industry experience also considered.